Production routeThis rate reflects platform-side availability — only confirmed server errors (HTTP 500 / empty response) count as failures. User-side issues (content moderation, invalid params, cancellation) plus rate limits, timeouts and auth errors are excluded. Before real traffic arrives, empty buckets may display as available.LiveLiveVision + language model
A native multimodal route that reads text and images in the same request, available on Chat Completions, Messages, and Responses — use whichever protocol your stack already speaks.
DeepSeek V4 Flash Vision
DeepSeek native multimodal model (text + image)
Selected
From $0.442 / 1M input tokensdeepseek-v4-flash-vision-exp
Best for
Screenshot and document understanding, chart and table extraction, OCR and UI pipelines, plus any text workload that occasionally needs to read an image.
Input
$0.442 / 1M
30 cr / 1M
Cached input
$0.015 / 1M
1 cr / 1M
Output
$1.324 / 1M
90 cr / 1M
DeepSeek V4 Flash Vision pricing
Estimate uncached input, cached input, and output tokens together. Images are billed as input tokens at the same rate — there is no image surcharge. Live prices for your user group override the contract-backed fallback rates.
DeepSeek V4 Flash Vision
Request calculator
Enter uncached input, cached input, and output tokens.
USD and credits are shown per 1M tokens. Live prices for your user group override the fallback rates. Minimum total charge: 0.01 credits.
DeepSeek V4 Flash Vision Exp API
Use the experimental deepseek-v4-flash-vision-exp model for mixed image-and-text tasks such as screenshot inspection, document extraction, chart analysis, and visual agents. EvoLink keeps pricing, usage, and model selection in one gateway; confirm the image-enabled protocols shown in the current API documentation before moving production traffic, and keep a verified fallback route.
EvoLink model IDdeepseek-v4-flash-vision-exp
Requests on Chat Completions, Messages, and Responses must use the exact model ID shown here.
Model specifications
Context window1,000,000 tokens
API protocolsChat Completions · Messages · Responses
Thinking modeOptional per request
Max output384,000 tokens
Token billingInput · cached input · output
Image inputPNG · JPEG · WebP · GIF
ProviderDeepSeek
Tasks and workflows
What is the DeepSeek V4 Flash Vision API best suited for?
DeepSeek V4 Flash Vision adds native image understanding to the V4 Flash route. Start with visual tasks that have clear acceptance criteria, then compare quality, latency, retries, and cost per accepted result before scaling.
Screenshot and UI inspection
Send product screenshots, dashboards, or error states with a focused question. Extract visible labels, identify layout or state differences, and return a structured QA summary. Validate small text, coordinates, and dense interfaces against a fixed screenshot set before automating decisions.
Document and receipt extraction
Turn scanned forms, invoices, receipts, and report pages into structured text for review or downstream processing. Define the expected JSON fields, preserve the source image for audit, and route low-confidence or missing fields to human review.
Chart and diagram analysis
Ask questions about charts, architecture diagrams, and technical figures while keeping the visual evidence in the same request. Require the response to distinguish observed labels from interpretation, especially when values are small or visually ambiguous.
Visual agents and UI verification
Combine screenshots with tool results so an agent can inspect a page state, propose the next action, or verify whether a workflow reached the expected screen. Keep actions reversible and evaluate task completion, latency, retries, and cost before expanding traffic.
API access choice
What changes when you access DeepSeek V4 Flash Vision through different platforms?
Even with the same model, platforms can differ in context configuration, tool support, usage reporting, and billing. EvoLink puts model configuration and usage behind one API so later model changes remain straightforward.
Use the correct API model ID
deepseek-v4-flash-vision-exp is the page URL and a common search form; the API request model ID is deepseek-v4-flash-vision-exp. Existing Chat Completions or Responses applications can use the correct ID in model configuration, with exact fields available in the API section.
Follow the active API route configuration
DeepSeek documents a 1M-token model window, but platforms may expose different context settings, tools, and rate limits. When using EvoLink, rely on the model configuration, available features, and actual usage shown for the current route.
Choose an image-input protocol by workflow
DeepSeek documents image inputs for Chat Completions, Messages, and Responses, but gateway exposure can differ. Use only the protocols and content-block formats currently listed for this model in EvoLink documentation, then test streaming, usage, and error behavior before rollout.
Keep model choice open in one gateway
Use one EvoLink account, balance, and API pattern for Grok, GPT, Claude, and Kimi. Keeping model selection in configuration lets teams route by task quality, cost, and availability without rebuilding application code for every provider.
Cost control
How can you control DeepSeek V4 Flash Vision API cost more accurately?
The existing Pricing section shows current token rates without duplicating a second price table. DeepSeek documents image tokenization at up to 384 input tokens per image; combine that rule with live EvoLink rates, output, retries, and cache behavior when estimating completed-task cost.
Budget image input before output
Use the official upper bound of 384 input tokens per image as a planning ceiling, then inspect actual usage on representative screenshots and documents. Multiple images, long prompts, output, and retries still contribute to the final charge.
Make repeated context cache-friendly
Stable system prompts, tool schemas, and shared context are easier to reuse through caching. Configure the supported cache or conversation identifier for the selected protocol and inspect cached tokens in usage to confirm that repeated requests receive the expected benefit.
Send only the context the task needs
A 1M-token window is useful for large repositories and documents, but it does not need to be filled on every request. Selecting only relevant files, messages, and retrieved passages reduces input cost and helps the model focus on the evidence that matters.
Compare models by total task cost
Evaluate tokens, cached input, server tools, and required retries within the same completed task. EvoLink centralizes model and usage information so teams can compare the total cost of completing equivalent work across DeepSeek V4 Flash Vision and other routes.
Production guidance
What should you confirm before using DeepSeek V4 Flash Vision in production?
Start with a small set of real workloads to determine whether the model meets your quality, latency, cost, and reliability needs before deciding which traffic should move.
Integration and usage data are clear
Confirm that the application uses the correct deepseek-v4-flash-vision-exp model ID and intended protocol, and that required responses, usage, and cache information are returned. Clear usage data supports cost analysis and gives Chat and Responses workflows a consistent observation method.
Visual outputs meet real business requirements
Test representative screenshots, forms, charts, and UI states. Check field accuracy, missing values, small-text handling, hallucinated details, structured-output validity, and the amount of human correction required before accepting a result.
Latency and error handling meet expectations
Observe response time under representative traffic and prepare retry behavior for rate limits, timeouts, invalid structured output, and tool failures. Configurable model selection in EvoLink makes it easier to switch to a verified alternative when one route is temporarily unavailable.
Cost and model choice remain controllable
Use Pricing, usage, and final charges to calculate total cost for the same class of task, then decide whether DeepSeek V4 Flash Vision belongs on the default route, difficult tasks, or fallback traffic. A unified gateway keeps quality and budget decisions separate from integration work.
Begin with a small, observable, reversible set of DeepSeek V4 Flash Vision tasks. Expand only after quality, latency, and cost meet expectations. Keeping multiple model options behind the EvoLink unified API makes later scaling, switching, and cost optimization easier.
Image-input protocol choice
How should you choose an image-input protocol and production route?
DeepSeek documents three image-capable protocol shapes. This section explains the decision; the current EvoLink API documentation remains the authority for which model IDs, endpoints, and content blocks are enabled on the gateway.
01
Chat Completions: familiar image_url content
Use Chat Completions when your application already follows the OpenAI-style messages array. Confirm that the Vision Exp model and image_url content type appear in the current EvoLink docs before using the request shape in production.
OpenAI-style chat
02
Messages: Anthropic-style image blocks
Use Messages only after EvoLink documentation explicitly lists image content blocks for deepseek-v4-flash-vision-exp. Include the documented max_tokens field and verify how usage, thinking content, and errors are returned.
Anthropic-style messages
03
Responses: multimodal agent input
Evaluate Responses when a longer agent workflow benefits from input_image and structured response handling. Confirm the active request fields, supported tools, streaming events, and retry boundary against EvoLink documentation.
Agent workflows
04
Production traffic: start small and keep a fallback
Send a small, observable set of tasks to DeepSeek V4 Flash Vision first and retain a proven GPT, Claude, or Kimi route. EvoLink’s unified API centralizes model choice, usage, and balance, making it easier to change routes after rate limits, timeouts, schema errors, or tool failures.
Gradual rollout
Related models
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OpenAI’s tiered frontier family for comparing capability, latency, and cost-routing flexibility.
What is the DeepSeek V4 Flash Vision Exp model ID?
The official model ID is deepseek-v4-flash-vision-exp. Keep the -exp suffix in configuration and treat it as an experimental route whose name, limits, or availability may change.
Is image input available through EvoLink?
Use the current EvoLink API documentation as the route authority. The upstream DeepSeek model accepts image input, but each EvoLink protocol and content-block format should be considered enabled only when it is listed in the current docs and passes a real request on your account.
Which image formats does the upstream model support?
DeepSeek documents JPEG, PNG, GIF, and WebP input. Check EvoLink documentation for gateway-specific URL, Base64, file-size, and multi-image requirements before production use.
How many input tokens does one image use?
DeepSeek documents a maximum of 384 input tokens per image. Use that as a planning ceiling, then inspect actual usage and the live EvoLink Pricing section because prompts, multiple images, output, retries, and cache behavior also affect the final charge.
Should I use Chat Completions, Messages, or Responses?
Choose the protocol your application already uses, provided EvoLink currently documents image support for this model on that protocol. Chat uses image_url-style content, Messages uses image blocks, and Responses uses input_image upstream; verify the active EvoLink request shape before implementation.
When should I use Vision Exp instead of V4 Flash?
Use Vision Exp when the request contains screenshots, scanned pages, charts, diagrams, or other image evidence. Keep deepseek-v4-flash for text-only, throughput-oriented workloads so the two pages and routes retain distinct jobs.
Can I rely on it for dense screenshots or small text?
Treat dense UI, small text, coordinates, and crowded tables as evaluation cases, not guaranteed strengths. Build a fixed test set, compare extracted fields against ground truth, and route low-confidence results to review.
What are the rate and file-size limits?
Do not infer limits from the text-only Flash route or the upstream API. Check the current EvoLink model documentation and dashboard for route-specific limits, then test 429, timeout, and oversized-image behavior before scaling.
How should teams compare Vision Exp with other vision models?
Run the same images, prompts, output schema, and review criteria across routes. Compare field accuracy, hallucinations, latency, retries, and total cost per accepted result rather than relying on a vendor benchmark claim.
What fallback should teams keep during rollout?
Keep a verified model that handles the same visual workload and leave model selection configurable. Start with a small traffic share, monitor errors and acceptance rate, and switch routes when the experimental model misses your production threshold.
